Because data is integral and important in the lives of people and in the operations of businesses, it is necessary to protect the data from failure. This is often achieved by backing up the data. In many instances, the amount of data that requires protection can be quite large. The problem of protecting large amounts of data was solved, in one example, using incremental backups. Once a full backup has been established, it no longer necessary to continually generate additional full backups every time a backup is generated. The backup process can be improved by generating incremental backups that can be combined if necessary to reproduce the data.
At the same time, it becomes more difficult to manage the backups as the number of incremental backups increase over time. Performance of the backup application may suffer in this circumstance. In addition, there may be limits on the number of incremental backups that are dependent on the system resources.
This problem is partially solved by generating synthetic backups. A synthetic backup is not generated per se from the actual data, but is generated by combining some of the backups. In effect, this allows a new full backup to be generated. This can reduce the number of backups and can simplify management of the backups.
Unfortunately, synthetic backups are often affected by various factors such as the operating system, the machines, and the way in which the data is stored. Some systems store data in big endian format and other systems store data in little endian format. This can complicate the versatility of the backups or save sets and can complicate the ability to restore a save set in a given computing environment.